Intensification of future heat waves in Pakistan: a study using CORDEX regional climate models ensemble

Future trends in the occurrence of heat waves (HW) over Pakistan have been presented using three regional climate models (RCMs), forced by three different global climate models (GCMs) runs under RCP8.5 scenarios. The results of RCMs are obtained from CORDEX (Coordinated Regional climate Downscaling EXperiment) database. Two different approaches for the assessment of HWs are defined, namely Fixed and Relative approaches. Fixed approach is defined for a life-threatening extreme event in which the temperature can reach more than 45 °C for a continuous stretch of several days; however, Relative approach events may not be directly life-threatening, but may cause snow/ice melt flooding and impact on food security of the country in summer and winter seasons, respectively. The results indicate a consistent increase in the occurrence of HWs for both approaches. For the Fixed approach, the increase is evident in the eastern areas of Pakistan, particularly plains of Punjab and Sindh provinces which host many big cities of the country. It is argued that the effect of HWs may also be exacerbated in future due to urban heat island effect. Moreover, summer time HWs for Relative approach is most likely to increase over northern areas of the country which hosts reservoirs of snow and glacier, which may result in events like glacial lake outburst flood and snow/ice melt flooding. Furthermore, the increase in winter time HWs for Relative approach may affect negatively on the wheat production, which in turn can distress the overall food productivity and livelihoods of the country. It is concluded that this study may be a useful document for future planning in order to better adapt to these threats due to climate change.     

Examination of chloritization of biotite as a tool for reconstructing the physicochemical parameters of mineralization and associated alteration in the Zafarghand porphyry copper system,Ardestan, Central Iran: mineral-chemistry and stable isotope analyses

Mineralogy and Petrology - The chloritization of biotite and stable isotopes of silicate have been studied for the Zafarghand porphyry copper deposit, Ardestan, Iran. The studied area, in the...     

Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

The prediction of wave parameters has a great significance in the coastal and offshore engineering. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, soft computing, and numerical based approaches. Recently, soft computing techniques such as recurrent neural networks (RNN) have been used to develop sea wave prediction models. In this study, the RNN for wave prediction based on the data gathered and the measurement of the sea waves in the Caspian Sea, in the north of Iran is used for this study. The efficiency of RNNs for 3, 6, and 12 hourly and diurnal wave prediction using correlation coefficients is calculated to be 0.96, 0.90, 0.87, and 0.73, respectively. This indicates that wave prediction by using RNNs yields better results than the previous neural network approaches.     

Local scour prediction around piers with complex geometry

A laboratory study of local scour at complex piers under steady clear-water conditions is presented. The term complex piers is used to define a bridge pier comprising of a column, pile cap, and pile group. Comprehensive data over the full range of possible pile cap elevations for complex piers with different geometries were obtained using five complex pier models, which were scaled down from existing bridges in Malaysia. The data are used to evaluate existing methodologies for characterizing the effective width of complex piers with varying pile cap location relative to the undisturbed streambed. The effect of pile cap location on scour depth is also addressed. To improve the predictions of local scour at complex piers, the new data and some previous data are used to propose a new method to predict local scour depth at complex piers.     

Experimental study on the phase transformation point of crushable and noncrushable soils

Two marine calcareous deposits as crushable soils and a siliceous sand as a noncrushable soil were used in this study to compare their monotonic response. Undrained monotonic triaxial tests were conducted on samples, which were prepared in different relative densities and consolidated under various confining pressures. The location of phase transformation point in undrained response of the sands in different initial conditions was evaluated. The effect of important parameters including relative density, confining pressure, particle shape, and particle breakage on phase transformation point was assessed. The input energy applied per unit volume of the soils was used to interpret the shearing response of crushable and noncrushable soils. The results showed that calcareous sands have more tendencies in contraction. Particle shape and breakage play a key role in engineering behavior of crushable soils.     

Applicability of Wakeby distribution in flood frequency analysis: a case study for eastern Australia

Parametric method of flood frequency analysis (FFA) involves fitting of a probability distribution to the observed flood data at the site of interest. When record length at a given site is relatively longer and flood data exhibits skewness, a distribution having more than three parameters is often used in FFA such as log‐Pearson type 3 distribution. This paper examines the suitability of a five‐parameter Wakeby distribution for the annual maximum flood data in eastern Australia. We adopt a Monte Carlo simulation technique to select an appropriate plotting position formula and to derive a probability plot correlation coefficient (PPCC) test statistic for Wakeby distribution. The Weibull plotting position formula has been found to be the most appropriate for the Wakeby distribution. Regression equations for the PPCC tests statistics associated with the Wakeby distribution for different levels of significance have been derived. Furthermore, a power study to estimate the rejection rate associated with the derived PPCC test statistics has been undertaken. Finally, an application using annual maximum flood series data from 91 catchments in eastern Australia has been presented. Results show that the developed regression equations can be used with a high degree of confidence to test whether the Wakeby distribution fits the annual maximum flood series data at a given station. The methodology developed in this paper can be adapted to other probability distributions and to other study areas. Copyright © 2014 John Wiley & Sons, Ltd.     

Fault diagnosis using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system(AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms(GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.     

Prediction of the Shear Wave Velocity from Compressional Wave Velocity for Gachsaran Formation

Shear and compressional wave velocities, coupled with other petrophysical data, are very important for hydrocarbon reservoir characterization. In situ shear wave velocity (Vs) is measured by some sonic logging tools. Shear velocity coupled with compressional velocity is vitally important in determining geomechanical parameters, identifying the lithology, mud weight design, hydraulic fracturing, geophysical studies such as VSP, etc. In this paper, a correlation between compressional and shear wave velocity is obtained for Gachsaran formation in Maroon oil field. Real data were used to examine the accuracy of the prediction equation. Moreover, the genetic algorithm was used to obtain the optimal value for constants of the suggested equation. Furthermore, artificial neural network was used to inspect the reliability of this method. These investigations verify the notion that the suggested equation could be considered as an efficient, fast, and cost-effective method for predicting Vs from Vp.